用完形填空评价俄语大学生的可读性:初步研究

J. D. Brown, G. Janssen, J. Trace, L. Kozhevnikova
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引用次数: 2

摘要

本初步研究考察了六项母语可读性指标与俄罗斯英语学生完形填空平均成绩之间的关系。完形填空是通过从美国公共图书馆中随机选择50篇文章,并删除每篇文章中每12个单词来创建一个30个项目的完形填空程序。参与者是来自俄罗斯联邦38所大学的5170名外语学生。每个学生被随机分配从30个填空段落中选择一个。50个传代的L1可读性指数分别为Flesch、Flesch- kincaid、Fry、Gunning、Fog和modified Gunning-Fog指数。初步结果表明,各指标之间存在中至高度的相关关系,但与俄罗斯大学生在完形填空版本上的平均成绩只有一定程度的相关关系。这些结果讨论了为什么L1可读性指数彼此之间具有中等到高度的相关性,而与俄语英语水平只有一定程度的相关性。作者还解释了他们在这些数据的进一步语言分析方面的计划(例如,平均单词长度、虚词百分比、每个句子的音节数、每个段落的单词数、段落中单词的频率等变量)和统计分析(至少包括因素分析、多元回归分析和结构方程建模)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
USING CLOZE PASSAGES TO ESTIMATE READABILITY FOR RUSSIAN UNIVERSITY STUDENTS: PRELIMINARY STUDY
This preliminary study examines the relationships between each of six first language (L1) readability indexes and the cloze passage mean performances of Russian EFL students. The cloze passages were created by randomly selecting 50 text passages from an American public library and deleting every 12th word in each passage to create a 30-item cloze procedure. The participants were 5170 EFL students from 38 universities in theRussian Federation. Each student was randomly assigned to take one of the 30-item cloze passages. The L1 readability indexes calculated for each of the 50 passages were the Flesch, Flesch-Kincaid, Fry, Gunning, Fog, and modified Gunning-Fog indexes. The preliminary results indicate that the L1 readability indexes were moderately to highly correlated with each other, but only somewhat correlated with the mean performances of Russian university students on cloze versions of those same passages. These results are discussed in terms of why the L1 readability indexes are moderately to highly correlated with each other but only somewhat correlated to the Russian EFL means. The authors also explain what they are planning in terms of further linguistic analyses (e.g., of variables like average word length, percent of function words, number of syllables per sentence, number of words per paragraph, frequencies of words in the passages, and so forth) and statistical analyses (including at least factor analysis, multiple regression analyses, and structural equation modeling) of these data. 
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